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检索条件"主题词=Reconstruction algorithm"

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COMPRESSIVE SPECTRAL IMAGING BASED ON COLORED CODED APERTURES

COMPRESSIVE SPECTRAL IMAGING BASED ON COLORED CODED APERTURE...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hoover Rueda Henry Arguello Gonzalo R. Arce Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (纸本)9781479928941

Compressive spectral imaging (CSI) systems capture the 3D spatio-spectral information of a scene by measuring 2D focal plane array (FPA) coded projections. A reconstruction algorithm exploiting the sparsity of the signal is then used to recover the underlying hyperspectral scene. CSI systems use a set of binary coded apertures, commonly realized through photomasks, to modulate the spatial characteristics of the scene. The reconstruction image quality in CSI is determined by the design of a 2D coded aperture binary set which block or unblock light from the scene onto the detector. This work extends the framework of CSI by replacing the traditional block-unblock photomasks by colored coded apertures which modulate the source not only spatially but spectrally as well. Simulations show a significant improvement in the quality of spectral image reconstructions.

关键词： apertures image reconstruction Photomasks Imaging reconstruction algorithm Quality Improvement ON-SCENE Image quality unlocking

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MULTI-SCALE/MULTI-RESOLUTION KRONECKER COMPRESSIVE IMAGING

MULTI-SCALE/MULTI-RESOLUTION KRONECKER COMPRESSIVE IMAGING

引用

IEEE International Conference on Image Processing

作者： Thuong Nguyen Canh Khanh Quoc Dinh Byeungwoo Jeon School of Electronic and Electrical Engineering Sungkyunkwan University

ISBN: (纸本)9781479983407

As a universal sampling procedure, compressive sensing (CS) considers that all samples of compressible signal are equally important. However, it is not true in in image/video signal since human visual system is more sensitive to low frequency components. Therefore, CS theory has been extended to hybrid and multi-scale CS to better capture the low-frequency samples. The computational complexity is another challenge in CS which can be solved by multi-resolution sensing matrix. In this paper, we propose a multi-scale/multi-resolution sensing matrix for Kronecker CS (KCS) based on separable wavelet transform and address measurement allocation problem with and without information of to-be-sensed image. The proposed methods not only perform better (3.72dB gain) but also low complexity and compatible with conventional reconstruction methods.

关键词： Kronecker compressive sensing Multi-scale Multi-resolution Measurement allocation Multi-resolution multi-scale CONTAINMENT SPRAY carbon monosulfide Cesium conditioned stimulus reconstruction algorithm Visual System Imaging wavelet transforms

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A Novel SAR Imaging Strategy Based on Compressed Sensing

A Novel SAR Imaging Strategy Based on Compressed Sensing

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Wentao Lv Junfeng Wang Wenxian Yu Department of Electronic Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781467311601

In this paper, we present a novel SAR (synthetic aperture radar) imaging algorithm based on compressed sensing (CS). We first obtain complex SAR images from raw SAR echoes, and then search all targets in complex-image domain by an improved MP (matching pursuit) algorithm. Unlike other CS-based imaging models, our algorithm has several advantages: 1) our algorithm is a two-dimensional (2-D) imaging system, rather than one-dimensional (1-D) imaging models introduced in most existing CS-based imaging algorithms;2) unlike low efficiencies of most reconstruction algorithms, our method has an efficient reconstruction rate;3) our imaging model applies existing SAR imaging systems, and inherits current SAR imaging results. The experimental results using real SAR echo data demonstrate the effectiveness of our algorithm.

关键词： Compressed sensing Complex-image domain Matching pursuit Point spread function estimation Radar polarimetry Compressed sensing reconstruction algorithm Rescue Synthetic aperture radar Specific absorption rate SAFETY ANALYSIS REPORTS algorithms Pursuit algorithms Imaging Echoes

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Spectral analysis of backscattered ultrasound field from hydroxyapatite granules

Spectral analysis of backscattered ultrasound field from hyd...

引用

4th IET International Conference on Advances in Medical, Signal and Information Processing - MEDSIP 2008

作者： Franco Marinozzi Daniele Piras Fabiano Bini University of Rome E2809CLa SapienzaE2809D Rome Italy

Microcalcifications (MCs) are of great importance in breast cancer early diagnosis and sometimes they do represent the unique evidence of cancer disease (up to 47%). Their visualization in ultrasound (US) is limited by a number of factors the most important of which are speckle and system limited spatial resolution. MCs are modelled as hydroxyapatite (HA) microgranules and sound scattering from HA microgranules can be solved by mean of Faran model. Agar based phantoms have been manufactured to simulate human soft tissues with cylindrical shape and a commercial echo-scanner (Technos Mpx di ESAOTE s.r.l., Genova, Italia) has been used to perform the scan of the phantom. This work concerns with phantoms and the analysis of US data for the visualization of HA microgranules under multiple angle scan. A reconstruction method was hence used to get a 2D map of reflectivity from the target and further Fourier analysis was applied to radio frequency (RF) data.

关键词： Hydroxyapatite Microcalcification Ultrasounds Breast Faran Ultrasonic Waves Microcalcification Durapatite Breast Spectrum Analysis Imaging Phantom Breast Cancer Visualization Paranormal phenomena Fourier analysis acoustic scattering scanning soft parts reconstruction algorithm

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Spectrum Sensing Based On Compressed Sensing

Spectrum Sensing Based On Compressed Sensing

引用

2011 International Conference on Computer, Electrical, and Systems Sciences, and Engineering (CESSE 2011)

作者： Shexiang Ma and Peng Zhang School of Computer and Communication Engineering Tianjin University of Technology Tianjin China

Spectrum sensing has become a key technology of cognitive radio. Wireless sensor network based is a good method. For large wireless sensor networks, the primary users are relatively fewer than the number of channels. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. This paper introduces a system model of spectrum sensing based on compressive sensing (CS). The primary users are considered to be sparse in the network. The number of sensors in work can be greatly reduced, which is much smaller than the total number of channels. Restricted Isometry Property (RIP) will ensure that the original spectrum signal can be reconstructed from less measurement data with high probability. Fuse center can recover the signal with Orthogonal Matching Pursuit (OMP). Differing from the previously works, spectrum signals are considered as continuous value. In the way, users can choose the desired channels by adjusting the threshold. Simulation results show that the scheme performs effectively. If the signal is operated an orthogonal transformation before measuring, the reconstructed results are better.

关键词： cognitive radio spectrum sensing measurement matrix sparse signal reconstruction algorithm

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Gradually Atom Pruning for Sparse reconstruction and Extension to Correlated Sparsity

Gradually Atom Pruning for Sparse Reconstruction and Extensi...

引用

Iranian Conference on Electrical Engineering

作者： Seyed Hossein Hosseini Mahrokh G. Shayesteh Department of Electrical Engineering Urmia University

ISBN: (纸本)9781467311496

We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of non-zero samples of sparse signal. We decompose each sample of signal into two variables, namely "value" and "detector", by a weighted exponential function. We update these new variables using gradient descent method. Like the traditional compressed sensing algorithms, the first variable is used to solve the Least Absolute Shrinkage and Selection Operator (Lasso) problem. As a new strategy, the second variable participates in the regularization term of the Lasso (l_1 norm) that gradually detects the non-zero elements. The presence of the second variable enables us to extend the corresponding vector of the first variable to matrix form. This makes possible use of the correlation matrix for a heuristic search in the case that there are correlations among the samples of signal. We compare the performance of the new algorithm with various algorithms for uncorrelated and correlated sparsity. The results indicate the efficiency of the proposed methods.

关键词： Smoothed l_0 L_1 minimization Compressed sensing reconstruction algorithm Correlated sparsity

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Ghost Imaging Lidar via Sparsity Constraints in Real Atmosphere

Ghost Imaging Lidar via Sparsity Constraints in Real Atmosph...

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The International Symposium on Photonics and Optoelectronics(SOPO 2013)

作者： Mingliang Chen Enrong Li Wenlin Gong Zunwang Bo Xuyang Xu Chengqiang Zhao Xia Shen Wendong Xu Shensheng Han Key Laboratory for Quantum Optics Center for Cold Atom Physics of CAS Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai

We present a series of results acquired at a 2-kilometer distance using our lidar system under several weather conditions, clear, cloudy, light rain, moderately foggy, and night. The experimental results show that gho... 详细信息

关键词： Ghost Imaging Lidar Atmosphere reconstruction algorithm

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Efficient Backprojection-based Synthetic Aperture Radar Computation with Many-core Processors 12

Efficient Backprojection-based Synthetic Aperture Radar Comp...

引用

ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis

作者： Jongsoo Park Ping Tak Peter Tang Mikhail Smelyanskiy Daehyun Kim Thomas Benson Intel Corporation Georgia Tech Research Institute

ISBN: (纸本)9781467308052

Tackling computationally challenging problems with high efficiency often requires the combination of algorithmic innovation, advanced architecture, and thorough exploitation of parallelism. We demonstrate this synergy through synthetic aperture radar (SAR) via backprojection, an image reconstruction method that can require hundreds of TFLOPS. Computation cost is significantly reduced by our new algorithm of approximate strength reduction; data movement cost is economized by software locality optimizations facilitated by advanced architecture support; parallelism is fully harnessed in various patterns and granularities. We deliver over 35 billion backprojections per second throughput per compute node on an Intel Xeon processor E5-2670-based cluster, equipped with Intel Xeon Phi coprocessors. This corresponds to processing a 3K×3K image within a second using a single node. Our study can be extended to other settings: backprojection is applicable elsewhere including medical imaging, approximate strength reduction is a general code transformation technique, and many-core processors are emerging as a solution to energy-efficient computing.

关键词： Synthetic aperture radar strength reduction PROCESSOR grain size parallelism backprojection Energy efficiency Building construction ARCHITECTURE Synergism arithmetic Synclitism reconstruction algorithm transcoding

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Compressed Sensing for Denoising in Adaptive System Identification

Compressed Sensing for Denoising in Adaptive System Identifi...

引用

Iranian Conference on Electrical Engineering

作者： Seyed Hossein Hosseini Mahrokh G. Shayesteh Department of Electrical Engineering Urmia University

ISBN: (纸本)9781467311496

We propose a new technique for adaptive identification of sparse systems based on the compressed sensing (CS) theory. We manipulate the transmitted pilot (input signal) and the received signal such that the weights of adaptive filter approach the compressed version of the sparse system instead of the original system. To this end, we use random filter structure at the transmitter to form the measurement matrix according to the CS framework. The original sparse system can be reconstructed by the conventional recovery algorithms. As a result, the denoising property of CS can be deployed in the proposed method at the recovery stage. The experiments indicate significant performance improvement of proposed method compared to the conventional LMS method which directly identifies the sparse system. Furthermore, at low levels of sparsity, our method outperforms a specialized identification algorithm that promotes sparsity.

关键词： Sparse system identification Compressed sensing reconstruction algorithm Random filter Least mean square

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REPRESENTING LAPLACIAN PYRAMIDS WITH VARYING AMOUNT OF REDUNDANCY

REPRESENTING LAPLACIAN PYRAMIDS WITH VARYING AMOUNT OF REDUN...

引用

European Signal Processing Conference

作者： Gagan Rath Christine Guillemot University of Pisa

The Laplacian pyramid (LP) is a useful tool for obtaining spatially scalable representations of visual signals such as image and video. However, the LP is overcomplete or redundant and has lower compression efficiency compared to critical representations such as wavelets and subband coding. In this paper, we propose to improve the rate-distortion (R-D) performance of the LP by varying its redundancy through decimation of the detail signals. We present two reconstruction algorithms based on the frame theory and the coding theory, and then show them to be equivalent. Simulation results with various standard test images suggest that, using suitable quantization parameters, it is possible to have better R-D performance over the usual or the dual frame based reconstruction.

关键词： redundancy reconstruction algorithm subband coding Shannon's theory Visual Signals test standards Laplacian pyramid wavelets Redundant

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